arXiv subjects

Cristian Spitoni

Publications and source records attributed to Cristian Spitoni.

At least 19 recordsLinked to original sources

arXiv2026

Topology, noise, and parallel updates in a model of circular opinion dynamics

We study a circular opinion dynamics model with local midpoint interactions, extended to allow parallel updates of multiple sites. On a ring, the dynamics admits twisted states associated with integer winding numbers. We investigate how bi-modal noise, which drives opinions toward two antipodal directions, affects these configurations. Numerically, we find that noise both destabilizes winding states and induces a flip--flop regime, characterized by macroscopic switching between preferred orientations. We introduce order parameters that distinguish topological trapping from symmetry breaking, providing a simple macroscopic description of the dynamics.

math.DS↗

arXiv2026

Topological trapping in circular midpoint opinion dynamics

We study a discrete-time asynchronous midpoint dynamics on the circle in which, at each step, a uniformly chosen neighboring pair moves to the midpoint along the shortest arc. Although the update rule is locally contractive, we show that the global relaxation mechanism depends sharply on the boundary topology. Under open boundary conditions the system converges almost surely to consensus through pure contraction. Under periodic boundary conditions the graph contains a single cycle, and the wrapped edge increments define an integer-valued winding number. While consensus remains the unique absorbing state for every fixed system size, we show that topology profoundly reshapes the transient dynamics. We prove that branch-crossings are the only mechanism capable of modifying the winding number and compute explicitly their probability for disordered initial data. Local averaging rapidly suppresses large gradients and drives the system into a no-branch-crossing regime where the winding number freezes. Inside a fixed winding sector we construct an adaptive co-moving frame in which the dynamics becomes an exact Euclidean midpoint process and establish strict contraction toward a twisted linear profile determined by the winding number. Our results isolate a minimal mechanism by which a single cycle induces sector locking and escape, even though the final equilibrium remains unchanged.

math.PR↗

arXiv2026

Inefficiency of the block approximation in diploid Probabilistic Cellular Automata

We study a probabilistic cellular automaton obtained as a mixture of the additive elementary rules 60 and 102. We prove that, for any finite periodic lattice and for mixing parameter $\lambda=1/2$, the system almost surely reaches the absorbing all-zero configuration in finitely many steps. In addition, Monte Carlo simulations indicate as well the presence of a zero-density stationary state in a finite interval around $\lambda=1/2$. Despite this absorbing behavior, both mean-field and block approximation schemes predict a stationary state with non-zero density. This failure, traced to the additive and mirror symmetries of the deterministic components, highlights a fundamental limitation of finite-block approximation in capturing the global dynamics of probabilistic cellular automata.

nlin.CG↗

arXiv2025

Optimal strategies for the growth of dual-seeded lattice structures

Optimal growth of structures governed by spatially stochastic dynamics arises in many scientific settings, for example in processes such as solution-based crystallization and the formation of microbial biofilms on patterned substrates or microfluidic networks. In this work, we investigate lattice growth using a two-dimensional, zero-temperature stochastic model of short-range spin interactions. Our goal is to determine how external perturbations can be optimized to steer the system efficiently toward the uniformly positive state, starting from two initial clusters of positive sites. To achieve this, we cast the problem as a Markov decision process adapted for a two-dimensional Ising model with zero-temperature dynamics. Within this framework, we compare alternative growth geometries and identify the structure of optimal strategies across three representative regimes.

math.OC↗

arXiv2024

Constructing Interpretable Prediction Models with 1D DNNs: An Example in Irregular ECG Classification

This manuscript proposes a novel methodology for developing an interpretable prediction model for irregular Electrocardiogram (ECG) classification, using features extracted by a 1-D Deconvolutional Neural Network (1-D DNN). Given the increasing prevalence of cardiovascular disease, there is a growing demand for models that provide transparent and clinically relevant predictions, which are essential for advancing the development of automated diagnostic tools. The features extracted by the 1-D DNN are included in a simple Logistic Regression (LR) model to predict abnormal ECG patterns. Our analysis demonstrates that the features are consistent with clinical knowledge and provide an interpretable and reliable classification of conditions such as Atrial Fibrillation (AF), Myocardial Infarction (MI), and Sinus Bradycardia Rhythm (SBR). Moreover, our findings show that the simple LR model has similar predictive accuracy to more complex models, such as a 1-D Convolutional Neural Network (1-D CNN), providing a concrete example of how to efficiently integrate Explainable Artificial Intelligence (XAI) methodologies with traditional regression models.

stat.AP↗

arXiv2024

Particle transport based study of nucleation in a ferromagnetic three-state spin system with conservative dynamics

We pose the problem of metastability for a three--state spin system with conservative dynamics. We consider the Blume--Capel model with the Kawasaki dynamics, we prove that, in a particular region of the parameter plane, the metastable state is the unique homogeneous minus state, and we estimate the exit time. To achieve our goal we have to solve several variational problems in the configuration space which result to be particularly involved, due to complicated structure of the trajectories. They key ingredient is the control of the energy differences between the configurations crossed when a spin is transported from the boundary to an internal site of the lattice through a completely arbitrary mixture of the three--state spin species. To master these mechanisms we have introduced a new approach based on the transport of spins along nearest neighbor connected regions of the lattice with constant spin configuration. This novel approach goes beyond the Blume--Capel model and can be used for the study of more general multi--state spin models.

math.PR↗

arXiv2024

Two-step interpretable modeling of Intensive Care Acquired Infections

We present a novel methodology for integrating high resolution longitudinal data with the dynamic prediction capabilities of survival models. The aim is two-fold: to improve the predictive power while maintaining interpretability of the models. To go beyond the black box paradigm of artificial neural networks, we propose a parsimonious and robust semi-parametric approach (i.e., a landmarking competing risks model) that combines routinely collected low-resolution data with predictive features extracted from a convolutional neural network, that was trained on high resolution time-dependent information. We then use saliency maps to analyze and explain the extra predictive power of this model. To illustrate our methodology, we focus on healthcare-associated infections in patients admitted to an intensive care unit.

stat.AP↗

arXiv2023

Homogeneous and heterogeneous nucleation in the three--state Blume--Capel model

The metastable behavior of the stochastic Blume--Capel model with Glauber dynamics is studied when zero-boundary conditions are considered. The presence of zero-boundary conditions changes drastically the metastability scenarios of the model: \emph{heterogeneous nucleation} will be proven in the region of the parameter space where the chemical potential is larger than the external magnetic field.

math-ph↗

arXiv2023

Learning models for classifying Raman spectra of genomic DNA from tumor subtypes

An early detection of different tumor subtypes is crucial for an effective guidance to personalized therapy. While much efforts focus on decoding the sequence of DNA basis to detect the genetic mutations related to cancer, it is becoming clear that physical properties, including structural conformation, stiffness, and shape, as well as biological processes, such as methylation, can be pivotal to recognize DNA modifications. Here we exploit the Surface Enhanced Raman Scattering (SERS) platform, based on disordered silver coated--silicon nanowires, to investigate genomic DNA from subtypes of melanoma and colon cancers and to efficiently discriminate tumor and healthy cells, as well as the different tumor subtypes. The diagnostic information is obtained by performing label--free Raman maps of the dried drops of DNA solutions onto the Ag/NWs mat, and leveraging the classification ability of learning models to reveal the specific and distinct interaction of healthy and tumor DNA molecules with nanowires.

stat.AP↗

arXiv2022

Metastability of synchronous and asynchronous dynamics

Metastability is an ubiquitous phenomenon in nature, which interests several fields of natural sciences. Its description in the framework of thermodynamics and statistical mechanics has been a taboo for long time since it is a genuine non--equilibrium phenomenon. Since the publication of the first seminal paper in which the metastable behavior of the mean field Curie--Weiss model was approached by means of stochastic techniques, this topic has been largely studied by the scientific community. Several papers and books have been published in which many different spin models were studied and different approaches were developed. In this review we focus on the comparison between the metastable behavior of synchronous and asynchronous dynamics, namely, stochastic processes in discrete time in which at each time either all the spins or one single spin are updated. In particular we discuss how the two different stochastic implementation of the very same Hamiltonian give rise to different metastable behaviors.

cond-mat.stat-mech↗

arXiv2021

Phase transitions in random mixtures of elementary cellular automata

We investigate one-dimensional Probabilistic Cellular Automata, called Diploid Elementary Cellular Automata (DECA), obtained as random mixture of two different Elementary Cellular Automata rules. All the cells are updated synchronously and the probability for one cell to be $0$ or $1$ at time $t$ depends only on the value of the same cell and that of its neighbors at time $t-1$. These very simple models show a very rich behavior strongly depending on the choice of the two Elementary Cellular Automata that are randomly mixed together and on the parameter which governs probabilistically the mixture. In particular, we study the existence of phase transition for the whole set of possible DECA obtained by mixing the null rule which associates $0$ to any possible local configuration, with any of the other $255$ elementary rule. We approach the problem analytically via a Mean Field approximation and via the use of a rigorous approach based on the application of the Dobrushin Criterion. The distinguishing trait of our result is the possibility to describe the behavior of the whole set of considered DECA without exploiting the local properties of the individual models. The results that we find are coherent with numerical studies already published in the scientific literature and also with some rigorous results proven for some specific models.

cond-mat.stat-mech↗

arXiv2019

Ergodicity versus non-ergodicity for Probabilistic Cellular Automata on rooted trees

In this article we study a class of shift-invariant and positive rate probabilistic cellular automata (PCA) on rooted d-regular trees $\mathbb{T}^d$. In a first result we extend the results of [10] on trees, namely we prove that to every stationary measure $ν$ of the PCA we can associate a space-time Gibbs measure $μ_ν$ on $\mathbb{Z} \times \mathbb{T}^d$. Under certain assumptions on the dynamics the converse is also true. A second result concerns proving sufficient conditions for ergodicity and non-ergodicity of our PCA on d-ary trees for $d\in \{ 1,2,3\}$ and characterizing the invariant product Bernoulli measures.

nlin.CG↗

arXiv2018

Nucleation for one-dimensional long-range Ising models

In this note we study metastability phenomena for a class of long-range Ising models in one-dimension. We prove that, under suitable general conditions, the configuration -1 is the only metastable state and we estimate the mean exit time. Moreover, we illustrate the theory with two examples (exponentially and polynomially decaying interaction) and we show that the critical droplet can be macroscopic or mesoscopic, according to the value of the external magnetic field.

cond-mat.stat-mech↗

arXiv2016

Sum of exit times in a series of two metastable states

We consider the problem of non degenerate in energy metastable states forming a series in the framework of reversible finite state space Markov chains. We assume that starting from the state at higher energy the system necessarily visits the second one before reaching the stable state. In this framework, we give a sharp estimate of the exit time from the metastable state at higher energy and, on the proper exponential time scale, we prove an addition rule. As an application of the theory, we study the Blume-Capel model in the zero chemical potential case.

math-ph↗

arXiv2016

Basic Ideas to Approach Metastability in Probabilistic Cellular Automata

Cellular Automata are discrete--time dynamical systems on a spatially extended discrete space which provide paradigmatic examples of nonlinear phenomena. Their stochastic generalizations, i.e., Probabilistic Cellular Automata, are discrete time Markov chains on lattice with finite single--cell states whose distinguishing feature is the \textit{parallel} character of the updating rule. We review some of the results obtained about the metastable behavior of Probabilistic Cellular Automata and we try to point out difficulties and peculiarities with respect to standard Statistical Mechanics Lattice models.

cond-mat.stat-mech↗

arXiv2016

Sum of exit times in series of metastable states in Probabilistic Cellular Automata

Reversible Probabilistic Cellular Automata are a special class of automata whose stationary behavior is described by Gibbs-like measures. For those models the dynamics can be trapped for a very long time in states which are very different from the ones typical of stationarity. This phenomenon can be recasted in the framework of metastability theory which is typical of Statistical Mechanics. In this paper we consider a model presenting two not degenerate in energy metastable states which form a series, in the sense that, when the dynamics is started at one of them, before reaching stationarity, the system must necessarily visit the second one. We discuss a rule for combining the exit times from each of the metastable states.

math-ph↗

arXiv2009

Competitive nucleation in metastable systems

Metastability is observed when a physical system is close to a first order phase transition. In this paper the metastable behavior of a two state reversible probabilistic cellular automaton with self-interaction is discussed. Depending on the self-interaction, competing metastable states arise and a behavior very similar to that of the three state Blume-Capel spin model is found.

cond-mat.stat-mech↗

arXiv2009

Metastability for reversible probabilistic cellular automata with self--interaction

The problem of metastability for a stochastic dynamics with a parallel updating rule is addressed in the Freidlin--Wentzel regime, namely, finite volume, small magnetic field, and small temperature. The model is characterized by the existence of many fixed points and cyclic pairs of the zero temperature dynamics, in which the system can be trapped in its way to the stable phase. %The characterization of the metastable behavior %of a system in the context of parallel dynamics is a very difficult task, %since all the jumps in the configuration space are allowed. Our strategy is based on recent powerful approaches, not needing a complete description of the fixed points of the dynamics, but relying on few model dependent results. We compute the exit time, in the sense of logarithmic equivalence, and characterize the critical droplet that is necessarily visited by the system during its excursion from the metastable to the stable state. We need to supply two model dependent inputs: (1) the communication energy, that is the minimal energy barrier that the system must overcome to reach the stable state starting from the metastable one; (2) a recurrence property stating that for any configuration different from the metastable state there exists a path, starting from such a configuration and reaching a lower energy state, such that its maximal energy is lower than the communication energy.

cond-mat.stat-mech↗